Min. 1st Qu. Median Mean 3rd Qu. Max.
-19.8663 -0.8485 -0.0663 0.1074 0.8228 29.6049 
results
Introduction
Effects of wildfire smoke on daily respiratory acute-care utilization has been estimated at the zip-code level in California (Do et al).
Rate differences per 100,000 are estimated at the zip code level.
Effect modification of this effect by community characteristics has been estimated.
The increase in risk difference per IQR increase in air conditioning prevalence is -0.239302618 (95% CI, -0.41143431, -0.0671709235), the corresponding IQR for AC prevalence is 6.000915e-01.
Over-arching question: How would hypothetically changing the distribution of the effect modifiers affect the number and distribution of respiratory acute-care utilization?
Baseline risk difference (zcta-level)
Histogram and summary statistics
RD is per 100,000
Map
Air conditioning (zcta-level)
Distribution of air conditioning at the zip-code level
Histogram and summary statistics
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
0.0000 0.2609 0.6250 0.5666 0.8710 1.0000 227 
Map
Methods
Scenario definitions:
Hypothetically change the distribution of AC in three ways to assess how it would change the risk difference at the ZCTA level.
| ac_25th | ac_50th | ac_75th | ac_100th | ac_iqr |
|---|---|---|---|---|
| 0.2608696 | 0.625 | 0.8709678 | 1 | 0.6100982 |
Scenario 1: Among those zip codes with an AC prevalence below the median, raise their AC prevalence to the median (0.6250).
Scenario 2: Among those zip codes with an AC prevalence below the 75th percentile, raise their AC prevalence to the 75th percentile (0.8710).
Scenario 3: Same, but 100th percentile (1).
HIA method
For each zip code, calculate the change in the AC proportion from the status quo to the target level. For example, if a zip code has a 50% AC prevalence, the difference from baseline to target in Scenario 1 is 12.5% (65.5%-50%). Then, express that difference in terms of the number of IQRs that it represents. The IQR of AC is 0.61 (above). That zip code would raise its AC prevalence by
0.125/.61[1] 0.204918Then, use that value to calculate the new risk difference under that scenario, following this equation:
rd_target_pt=rd_baseline_pt+rd_per_ac_iqr_pt*ac_prop_change_per_iqr
where * rd_target_pt = the zip code’s new risk difference under the scenario
rd_per_ac_iqr_pt = the increase in the risk difference per change in IQR of AC
ac_prop_change_per_iqr = the number of IQRs changed in that zip code in that scenario
This assumes that the increase is linear.
Uncertainty
Calculate uncertainty by re-sampling rd_baseline_pt and rd_per_ac_iqr_pt from a normal distribution with 1,000 bootstrap replications. Uncertainty interval is 2.5th and 97.5th percentiles.
Results
Overall summary
There are 1,396 total zip codes
n_zcta_intervene is the number of zip codes whose AC values would be changed under the scenario.
Risk differences are expressed per 100,000.
Means and sums are for those zctas with intervention
| target_percentile | n_zcta_intervene | rd_baseline_sum | rd_baseline_sum_ll | rd_baseline_sum_ul | rd_baseline_mean_unwt | rd_baseline_mean_unwt_ll | rd_baseline_mean_unwt_ul | rd_target_sum | rd_target_sum_ll | rd_target_sum_ul | rd_target_mean_unwt | rd_target_mean_unwt_ll | rd_target_mean_unwt_ul | rd_target_v_baseline_diff_in_sum | rd_target_v_baseline_diff_in_sum_ll | rd_target_v_baseline_diff_in_sum_ul |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.50 | 580 | 129.604 | 51.876 | 208.044 | 0.223 | 0.089 | 0.359 | 49.258 | 82.215 | 236.335 | 0.085 | 0.142 | 0.407 | 80.345 | -32.392 | -26.742 |
| 0.75 | 876 | 144.325 | 53.005 | 246.992 | 0.165 | 0.061 | 0.282 | -5.982 | 109.718 | 300.287 | -0.007 | 0.125 | 0.343 | 150.307 | -59.610 | -50.506 |
| 1.00 | 1013 | 121.820 | 21.253 | 223.076 | 0.120 | 0.021 | 0.220 | -76.922 | 92.033 | 296.173 | -0.076 | 0.091 | 0.292 | 198.743 | -78.332 | -67.459 |